DisSim-FinBERT: Text Simplification for Core Message Extraction in Complex Financial Texts

TL;DR

DisSim-FinBERT is a framework combining discourse simplification with sentiment analysis to improve interpretation of complex financial texts, aiding policymakers and analysts in extracting accurate insights from detailed economic documents.

Contribution

The paper introduces DisSim-FinBERT, a novel approach that enhances sentiment analysis in financial texts by integrating discourse simplification with aspect-based sentiment analysis.

Findings

Improved accuracy in sentiment prediction for complex financial texts

Enhanced aspect identification through text simplification

Better alignment of sentiment analysis with economic events

Abstract

This study proposes DisSim-FinBERT, a novel framework that integrates Discourse Simplification (DisSim) with Aspect-Based Sentiment Analysis (ABSA) to enhance sentiment prediction in complex financial texts. By simplifying intricate documents such as Federal Open Market Committee (FOMC) minutes, DisSim improves the precision of aspect identification, resulting in sentiment predictions that align more closely with economic events. The model preserves the original informational content and captures the inherent volatility of financial language, offering a more nuanced and accurate interpretation of long-form financial communications. This approach provides a practical tool for policymakers and analysts aiming to extract actionable insights from central bank narratives and other detailed economic documents.